Comparison of manifold learning algorithms used in FSI data interpolation of curved surfaces
Multidiscipline Modeling in Materials and Structures
ISSN: 1573-6105
Article publication date: 14 August 2017
Abstract
Purpose
The purpose of this paper is to discuss a data interpolation method of curved surfaces from the point of dimension reduction and manifold learning.
Design/methodology/approach
Instead of transmitting data of curved surfaces in 3D space directly, the method transmits data by unfolding 3D curved surfaces into 2D planes by manifold learning algorithms. The similarity between surface unfolding and manifold learning is discussed. Projection ability of several manifold learning algorithms is investigated to unfold curved surface. The algorithms’ efficiency and their influences on the accuracy of data transmission are investigated by three examples.
Findings
It is found that the data interpolations using manifold learning algorithms LLE, HLLE and LTSA are efficient and accurate.
Originality/value
The method can improve the accuracies of coupling data interpolation and fluid-structure interaction simulation involving curved surfaces.
Keywords
Citation
Liu, M.-m., Li, L.Z. and Zhang, J. (2017), "Comparison of manifold learning algorithms used in FSI data interpolation of curved surfaces", Multidiscipline Modeling in Materials and Structures, Vol. 13 No. 2, pp. 217-261. https://doi.org/10.1108/MMMS-07-2016-0032
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited